Semi-supervised learning:
Among the various levels of supervision that have been explored in the community, semi-supervised learning approaches [1]} try to achieve a good balance between maximizing accuracy while minimizing human input. A commonly used semi-supervised technique is the bootstrap or self-learning approach [1]} where an agent initially learns from a small amount of labeled data. It then retrieves images from a large unlabeled pool whose labels it is most confident of, and transfers these to the labeled set to re-learn its model. Such approaches often suffer from semantic drift [3]}. Recently, visual attributes have been successfully used to resist this drift [4]}. This approach however relies on a human annotating all attribute-category relationships offline. In our approach, the machines communicate these attribute-category relationships to each other, alleviating the need for human involvement. Moreover, the attribute-category relationships estimated by machines are soft and more robust. Human annotated attribute-category relationships tend to be hard [4]}, [6]}, ignoring instance-level variations within categories.
